1. Field of the Invention
The present invention relates to a speech processing apparatus and a method for recognizing input speech. More particularly, the present invention relates to a speech processing apparatus and a method capable of handling speech input in a noisy environment.
2. Description of the Related Art
In recent years, in order to make speech recognition practical, preventing noise in the environment of use from interfering with speech recognition has been actively researched. Among the research, a PMC method capable of producing a noise-adaptive speech model in which a speech model is adapted from a small amount of noise data has been proposed.
FIG. 7 is a conceptual view illustrating the flow of a noise adaptation process in a PMC method.
As shown in FIG. 7, in a noise adaptation process in the PMC method, initially, cosine transform processes 601 and 602 and exponential transform processes 603 and 604 are performed in sequence on a speech model (speech HMM (Hidden Markov Model)) and a noise model (noise HMM), respectively, and a process 605 for synthesizing the results is performed. Then, by performing a logarithmic transform process 606 and an inverse cosine transform process 607 on the synthesized result, a noise-adaptive speech model (PMC-HMM) is obtained.
Circles are schematic representations of the HMM state. Arrows are schematic representations of the state transition. The cluster of circles and arrows are schematic representations of the HMM model. The serrations represent the waveform of sound. The left upper part of the figure shows the phoneme model and the waveform thereof. The left lower part of the figure shows a noise model and the waveform thereof. The right upper part of the figure shows a noise-superposed speech model and the waveform thereof.
However, in this PMC method, if the number of types of speech models is increased or the number of distributions is increased to increase the recognition performance, a very large amount of processing is required because all distributions are PMC-converted in a conventional PMC method.
As described above, in a conventional speech recognition method, when a detailed speech model (a majority model and a majority distribution) is noise-adapted by using a small amount of noise data by a PMC method, there arises the problem that a very large amount of processing is required because all distributions are PMC-converted in the conventional PMC method.